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[arXiv]
[bibtex]@InProceedings{Lee_2024_CVPR, author = {Lee, Minhyeok and Cho, Suhwan and Lee, Dogyoon and Park, Chaewon and Lee, Jungho and Lee, Sangyoun}, title = {Guided Slot Attention for Unsupervised Video Object Segmentation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {3807-3816} }
Guided Slot Attention for Unsupervised Video Object Segmentation
Abstract
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground-background separation. The foreground and background slots which are initialized with query guidance are iteratively refined based on interactions with template information. Furthermore to improve slot-template interaction and effectively fuse global and local features in the target and reference frames K-nearest neighbors filtering and a feature aggregation transformer are introduced. The proposed model achieves state-of-the-art performance on two popular datasets. Additionally we demonstrate the robustness of the proposed model in challenging scenes through various comparative experiments.
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